!12025 fix the example of Unique, SparseApplyProximalAdagrad, BoundingBoxEncode, SGD and Parameter.

From: @wangshuide2020
Reviewed-by: @liangchenghui,@wuxuejian
Signed-off-by: @liangchenghui

mindspore/common/parameter.py

@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2021 Huawei Technologies Co., Ltd
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -95,17 +95,16 @@ class Parameter(Tensor_):
         ...     def __init__(self):
         ...         super(Net, self).__init__()
         ...         self.matmul = P.MatMul()
-        ...         self.weight = Parameter(Tensor(np.ones((1,2))), name="w", requires_grad=True)
+        ...         self.weight = Parameter(Tensor(np.ones((1, 2)), mindspore.float32), name="w", requires_grad=True)
         ...
         ...     def construct(self, x):
         ...         out = self.matmul(self.weight, x)
         ...         return out
         >>> net = Net()
-        >>> x = Tensor(np.ones((2,1)))
+        >>> x = Tensor(np.ones((2, 1)), mindspore.float32)
         >>> print(net(x))
         [[2.]]
-        >>> net.weight.set_data(Tensor(np.zeros((1,2))))
-        Parameter (name=w, shape=(1, 2), dtype=Float64, requires_grad=True)
+        >>> _ = net.weight.set_data(Tensor(np.zeros((1, 2)), mindspore.float32))
         >>> print(net(x))
         [[0.]]
     """

mindspore/nn/layer/normalization.py

@@ -269,7 +269,7 @@ class BatchNorm1d(_BatchNorm):
         Tensor, the normalized, scaled, offset tensor, of shape :math:`(N, C_{out})`.
 
     Supported Platforms:
-        ``Ascend`` ``GPU``
+        ``Ascend``
 
     Raises:
         TypeError: If `num_features` is not an int.

mindspore/ops/_utils/utils.py

@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2021 Huawei Technologies Co., Ltd
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -33,7 +33,7 @@ def get_broadcast_shape(x_shape, y_shape, prim_name):
         List, the shape that broadcast between tensor x and tensor y.
 
     Raises:
-        ValueError: If tensor x and tensor y are not equal and could't broadcast.
+        ValueError: If tensor x and tensor y are not equal and couldn't broadcast.
 
     Examples:
         >>> x_shape = [1, 2, 3]
@@ -66,7 +66,7 @@ def get_broadcast_shape(x_shape, y_shape, prim_name):
 
 def get_concat_offset(x_shp, x_type, axis, prim_name):
     """for concat and concatoffset check args and compute offset"""
-    validator.check_value_type("shape", x_shp, [tuple], prim_name)
+    validator.check_value_type("shape", x_shp, [tuple, list], prim_name)
     validator.check_positive_int(len(x_shp), "input_x rank", prim_name)
     validator.check_subclass("shape0", x_type[0], mstype.tensor, prim_name)
     validator.check_positive_int(len(x_shp[0]), "len of x_shp[0]", prim_name)

mindspore/ops/operations/array_ops.py

@@ -758,14 +758,14 @@ class Unique(Primitive):
         >>> class UniqueNet(nn.Cell):
         ...     def __init__(self):
         ...         super(UniqueNet, self).__init__()
-        ...         self.unique_op = P.Unique()
+        ...         self.unique_op = ops.Unique()
         ...
         ...     def construct(self, x):
         ...         output, indices = self.unique_op(x)
         ...         return output, indices
         ...
         >>> x = Tensor(np.array([1, 2, 5, 2]), mindspore.int32)
-        >>> context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
+        >>> context.set_context(mode=context.GRAPH_MODE)
         >>> net = UniqueNet()
         >>> output = net(x)
         >>> print(output)

mindspore/ops/operations/nn_ops.py

@@ -2677,7 +2677,7 @@ class SGD(PrimitiveWithCheck):
         >>> momentum = Tensor(0.1, mindspore.float32)
         >>> stat = Tensor(np.array([1.5, -0.3, 0.2, -0.7]), mindspore.float32)
         >>> output = sgd(parameters, gradient, learning_rate, accum, momentum, stat)
-        >>> print(output[0])
+        >>> print(output)
         (Tensor(shape=[4], dtype=Float32, value= [ 1.98989999e+00, -4.90300000e-01,  1.69520009e+00,  3.98009992e+00]),)
     """
 
@@ -5629,14 +5629,14 @@ class SparseApplyProximalAdagrad(PrimitiveWithCheck):
         ...
         >>> net = Net()
         >>> grad = Tensor(np.array([[1, 1], [1, 1]], np.float32))
-        >>> indices = Tensor(np.array([0], np.int32))
+        >>> indices = Tensor(np.array([0, 1], np.int32))
         >>> output = net(grad, indices)
         >>> print(output)
         (Tensor(shape=[2, 2], dtype=Float32, value=
-        [[ 2.97499990e+00,  6.07499981e+00],
-         [ 0.00000000e+00,  1.87500000e+00]]), Tensor(shape=[2, 2], dtype=Float32, value=
-        [[ 6.40000000e+01,  6.40000000e+01],
-         [ 6.40000000e+01,  6.40000000e+01]]))
+        [[ 2.09999990e+00,  5.199999981e+00],
+         [ 0.00000000e+00,  1.000000000e+00]]), Tensor(shape=[2, 2], dtype=Float32, value=
+        [[ 1.00000000e+00,  1.000000000e+00],
+         [ 1.00000000e+00,  1.000000000e+00]]))
     """
 
     __mindspore_signature__ = (

mindspore/ops/operations/other_ops.py

@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2021 Huawei Technologies Co., Ltd
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -133,8 +133,8 @@ class BoundingBoxEncode(PrimitiveWithInfer):
         >>> boundingbox_encode = ops.BoundingBoxEncode(means=(0.0, 0.0, 0.0, 0.0), stds=(1.0, 1.0, 1.0, 1.0))
         >>> output = boundingbox_encode(anchor_box, groundtruth_box)
         >>> print(output)
-        [[ －1.  0.25  0.  0.40551758]
-         [ －1.  0.25  0.  0.40551758]]
+        [[ -1.  0.25  0.  0.40551758]
+         [ -1.  0.25  0.  0.40551758]]
     """
 
     @prim_attr_register